Implantable and external electrical stimulators assist thousands of individuals with neurological disorders. These stimulators generate electrical Waveforms, Which are delivered by leads to targeted tissue regions to treat the neurological disorders. Examples of treating neurological disorders using electrical stimulation include deep brain stimulation, cortical stimulation, vagus nerve stimulation, sacral nerve stimulation, spinal cord stimulation, and cardiac pace makers and defibrillators.
Implantable stimulators are powered by either primary cell or rechargeable batteries. When the energy of a primary cell battery is depleted, the entire stimulator must be replaced through an expensive and invasive surgical procedure. The energy capacity of a rechargeable battery determines the recharge interval, as Well as the overall volume of the implant.
There are clinical benefits to reducing the frequency of battery-replacement surgeries or recharge intervals, as Well as reducing the physical size (volume) of the stimulator itself. The problem is how one alters stimulation parameters to achieve this objective without sacrificing clinical efficacy and generating unwanted side effects. For example, the energy efficiency of stimulation (i.e., how much energy is consumed for the generation of a given stimulation pulse) cannot be viewed in isolation. The charge efficiency of stimulation is also an important consideration with implanted devices. The charge delivered during a stimulus pulse contributes to the risk of tissue damage (Yuen et al. 1981; McCreery et al. 1990). If energy-efficient stimulation parameters deliver excessive amounts of charge, then the benefits of high energy efficiency are diminished.
As shown in FIGS. 1A and 1B, the energy efficiency of stimulation parameters is dependent upon the amplitude of the stimulation pulse (typically expressed, e.g., in a range from 10 μA upwards to 10 mA); the width or duration of the stimulation pulse (typically expressed, e.g., in a range from 20 μs upwards to 500 μs); the frequency of the pulses applied over time (typically expressed, e.g., in a range from 10 Hz upwards to 200 Hz); and the shape or waveform of the pulse (e.g., typically, depending upon the therapeutic objective, square (rectangular) (see FIG. 2A), or rising ramp (see FIG. 2B), or sinusoid (see FIG. 2C), or decreasing exponential (see FIG. 2D), or rising exponential (see FIG. 2E)).
Previous studies have used passive membrane models to analyze the effects of waveform shape on efficiency. All previous studies using passive membrane models have concluded that the energy-optimal waveform shape is a rising exponential (Offner 1946; Fishler 2000; Kajimoto et al. 2004; Jezernik and Morari 2005).
However, in more realistic models and in vivo experiments, the inventors have found that the rising exponential waveform proved to be no more energy-efficient than rectangular, ramp, or decaying exponential waveforms. In fact, in realistic membrane models, the inventors have found that energy-optimal Waveform shapes cannot be determined analytically because of the complexity and non-linearity of the equations that define the excitable membrane in the model. Also, a “brute force” method of testing every possible waveform shape is not feasible since the number of possible waveform shapes is infinite.